Background: While Diabetic Foot Ulcer (DFU) prevention and treatment have improved dramatically, assessment of DFU healing remains outdated, presenting a problem for research and clinical practice. Recent new mobile applications for standardizing DFU images have been reported, however, they are not automated and require large manual efforts. Aim: This study aims to define a reliable and sensitive metric by using machine learning to quantify wound severity, taking into account its size, shape, ischaemic and infection severity using a standard 2D photograph. This single metric could be used to identify and triage non-healing DFUs, as well as track wound healing in response to treatment. Method: Serial photographs of DFUs in 28 patients (90% T2DM, mean age 63, 83% male) with varying severity of DFU (72% UT 1A-C), were collected and analysed. Images from sequential clinic visits were automatically aligned. Wounds were automatically localized and segmented. Results: We have characterized DFUs and quantified changes in DFU characteristics across visits. 91% of baseline images were correctly identified and localized by the artificial intelligence (AI)-based wound localisation method. Discussion: If successful, this measure will facilitate targeting of appropriate therapies to individual patients, improving healing outcomes and therefore resulting in fewer amputations.